class_docstr: Class for the Fisher information metric on geometric distributions.
  
  References
  ----------
  .. [AM1981] Atkinson, C., & Mitchell, A. F. (1981). Rao's distance measure.
      Sankhyā: The Indian Journal of Statistics, Series A, 345-365.
docstr: Compute the metric matrix at base_point.
  
  Parameters
  ----------
  base_point : array-like, shape=[..., 1]
      Point representing a geometric distribution.
  
  Returns
  -------
  mat : array-like, shape=[..., 1, 1]
      Metric matrix.
docstr_tok: ["Compute","the","metric","matrix","at","base_point",".","Parameters","--","--","--","--","--","base_point",":","array-like",",","shape=","[","...",",","1","]","Point","representing","a","geometric","distribution",".","Returns","--","--","--","-","mat",":","array-like",",","shape=","[","...",",","1",",","1","]","Metric","matrix","."]
code: def metric_matrix(self, base_point):
      """Compute the metric matrix at base_point.
  
      Parameters
      ----------
      base_point : array-like, shape=[..., 1]
          Point representing a geometric distribution.
  
      Returns
      -------
      mat : array-like, shape=[..., 1, 1]
          Metric matrix.
      """
      return gs.expand_dims(
          1 / (base_point**2 * (1 - base_point)), axis=-1
      )
  
imports: from scipy.stats import geom
  import geomstats.backend
  from geomstats.geometry.base import VectorSpaceOpenSet
  from geomstats.geometry.euclidean import Euclidean
  from geomstats.geometry.riemannian_metric import RiemannianMetric
  from geomstats.information_geometry.base import InformationManifoldMixin, ScipyUnivariateRandomVariable
label_desc: Use image node_id 2 for calling the GeometricMetric obj's underlying member method code with example usage: obj.metric_matrix(base_point) and returns: gs